By applying non-equilibrium Green's function in combination with density functional theory,we investigated the electronic transport properties of capped-carbon-nanotube-based molecular junctions with multiple N an...By applying non-equilibrium Green's function in combination with density functional theory,we investigated the electronic transport properties of capped-carbon-nanotube-based molecular junctions with multiple N and B dopants.The results show that the electronic transport properties are strongly dependent on the numbers and positions of N and B dopants.Best rectifying behavior is observed in the case with one N and one B dopants,and it is deteriorated strongly with the increasing dopants.The rectifying direction is even reversed with the change of doping positions.Moreover,obvious negative differential resistance behavior at very low bias is observed in some doping cases.展开更多
Currently,there are many limitations to classify images of small objects.In addition,there are limitations such as error detection due to external factors,and there is also a disadvantage that it is difficult to accur...Currently,there are many limitations to classify images of small objects.In addition,there are limitations such as error detection due to external factors,and there is also a disadvantage that it is difficult to accurately distinguish between various objects.This paper uses a convolutional neural network(CNN)algorithm to recognize and classify object images of very small moths and obtain precise data images.A convolution neural network algorithm is used for image data classification,and the classified image is transformed into image data to learn the topological structure of the image.To improve the accuracy of the image classification and reduce the loss rate,a parameter for finding a fast-optimal point of image classification is set by a convolutional neural network and a pixel image as a preprocessor.As a result of this study,we applied a convolution neural network algorithm to classify the images of very small moths by capturing precise images of the moths.Experimental results showed that the accuracy of classification of very small moths was more than 90%.展开更多
基金supported by the National Natural Science Foundation of China (11104115, 11074146)the Natural Science Foundation of Shan-dong Province of China (ZR2009AL004)the Doctoral Foundation of University of Jinan (XBS1004)
文摘By applying non-equilibrium Green's function in combination with density functional theory,we investigated the electronic transport properties of capped-carbon-nanotube-based molecular junctions with multiple N and B dopants.The results show that the electronic transport properties are strongly dependent on the numbers and positions of N and B dopants.Best rectifying behavior is observed in the case with one N and one B dopants,and it is deteriorated strongly with the increasing dopants.The rectifying direction is even reversed with the change of doping positions.Moreover,obvious negative differential resistance behavior at very low bias is observed in some doping cases.
文摘Currently,there are many limitations to classify images of small objects.In addition,there are limitations such as error detection due to external factors,and there is also a disadvantage that it is difficult to accurately distinguish between various objects.This paper uses a convolutional neural network(CNN)algorithm to recognize and classify object images of very small moths and obtain precise data images.A convolution neural network algorithm is used for image data classification,and the classified image is transformed into image data to learn the topological structure of the image.To improve the accuracy of the image classification and reduce the loss rate,a parameter for finding a fast-optimal point of image classification is set by a convolutional neural network and a pixel image as a preprocessor.As a result of this study,we applied a convolution neural network algorithm to classify the images of very small moths by capturing precise images of the moths.Experimental results showed that the accuracy of classification of very small moths was more than 90%.
